import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly
import plotly.express as px
Confucian Filial Piety: The Confucian virtue of filial piety (孝xiao) stresses respect for parents, elders and ancestors, as well as emphasized patriarchal family structures where wives served their husbands and mothers served their sons. Confucianism conceptualized the family as analogous to the human body or a living tree, where every role in the family was a distinct component of an inseparable entity. A filial son is obedient and loyal; a father is strict and benevolent.
Modernization Theory: Debunked in the 1970s. As a society becomes more industrialized and modern, the Modernization Theory argues that the role of the individual increases in importance and the family decreases in importance.
Each survey has approximately 1000 respondents with 400+ Questions.
df1990 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/1990.csv')
df1995 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/1995.csv')
df2001 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2001.csv')
df2007 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2007.csv')
df2012 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2012.csv')
df2018 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2018.csv')
pd.set_option('max_columns', None)
df1990
| V1: WVS wave | V2: Country/region | V2A: Country/region [with split ups] | V3: Original respondent number | V4: Important in life: Work | V5: Important in life: Family | V6: Important in life: Friends | V7: Important in life: Leisure time | V8: Important in life: Politics | V9: Important in life: Religion | V10: How often discusses political matters with friends | V11: Persuading friends, relatives or fellow workers | V12: Would give part of my income for the environment | V13: Increase in taxes if used to prevent environmental pollution | V14: Government should reduce environmental pollution | V15: All talk about the environment make people anxious | V16: Combatting unemployment, we have to accept environmental problems | V17: Protecting environment and fighting pollution is less urgent than suggested | V18: Feeling of happiness | V19: Member: Belong to social welfare service for elderly | V20: Member: Belong to religious organization | V21: Member: Belong to education, arts, music or cultural activities | V22: Member: Belong to labour unions | V23: Member: Belong to political parties | V24: Member: Belong to local political actions | V25: Member: Belong to human rights | V26: Member: Belong to conservation, the environment, ecology | V27: Member: Belong to professional associations | V28: Member: Belong to youth work | V29: Member: Belong to sports or recreation | V30: Member: Belong to women´s group | V31: Member: Belong to peace movement | V32: Member: Belong to animal rights | V33: Member: Belong to organization concerned with health | V34: Member: Belong to other groups | V35: Member: Belong to none | V36: Member: Don´t know | V37: Voluntary work: Unpaid work social welfare service for elderly, handicapped or deprived people | V38: Voluntary work: Unpaid work religious or church organization | V39: Voluntary work: Unpaid work education, arts, music or cultural activities | V40: Voluntary work: Unpaid work labour unions | V41: Voluntary work: Unpaid work political parties or groups | V42: Voluntary work: Unpaid work local political action groups | V43: Voluntary work: Unpaid work human rights | V44: Voluntary work: Unpaid work environment, conservation, ecology | V45: Voluntary work: Unpaid work professional associations | V46: Voluntary work: Unpaid work youth work | V47: Voluntary work: Unpaid work sports or recreation | V48: Voluntary work: Unpaid work women´s group | V49: Voluntary work: Unpaid work peace movement | V50: Voluntary work: Unpaid work animal rights | V51: Voluntary work: Unpaid work organization concerned with health | V52: Voluntary work: Unpaid work other groups | V53: Voluntary work: Unpaid work none | V54: Voluntary work: Don´t know | V55: Reasons voluntary work: Solidarity with the poor and disadvantaged | V56: Reasons voluntary work: Compassion for those in need | V57: Reasons voluntary work: Opportunity to repay something | V58: Reasons voluntary work: Sense of duty, moral, obligation | V59: Reasons voluntary work: Identifying with people who suffer | V60: Reasons voluntary work: Time on my hands | V61: Reasons voluntary work: Personal satisfaction | V62: Reasons voluntary work: Religious belief | V63: Reasons voluntary work: Help disadvantaged people | V64: Reasons voluntary work: Make a contribution to my local community | V65: Reasons voluntary work: Bring about social or political change | V66: Reasons voluntary work: For social reasons | V67: Reasons voluntary work: Gain new skills and useful experience | V68: Reasons voluntary work: Did not want to, but could not refuse | V69: Neighbours: People with a criminal record | V70: Neighbours: People of a different race | V71: Neighbours: Left wing extremists | V72: Neighbours: Heavy drinkers | V73: Neighbours: Right wing extremists | V74: Neighbours: People with large families | V75: Neighbours: Emotionally unstable people | V76: Neighbours: Muslims | V77: Neighbours: Immigrants/foreign workers | V78: Neighbours: People who have AIDS | V79: Neighbours: Drug addicts | V80: Neighbours: Homosexuals | V81: Neighbours: Jews | V82: Neighbours: Hindus | V82A: Neighbours: Political Extremists | V83: State of health (subjective) | V84: Ever felt very excited or interested | V85: Ever felt restless | V86: Ever felt proud because someone complimented you | V87: Ever felt very lonely or remote from other people | V88: Ever felt pleased about having accomplished something | V89: Ever felt bored | V90: Ever felt on top of the world | V91: Ever felt depressed or very unhappy | V92: Ever felt that things were going your way | V93: Ever felt upset because somebody criticized you | V94: Most people can be trusted | V95: How much freedom of choice and control | V96: Satisfaction with your life | V97: Why are there people living in need: first | V98: Why are there people living in need: second | V99: Important in a job: good pay | V100: Important in a job: pleasant people to work with | V101: Important in a job: not too much pressure | V102: Important in a job: good job security | V103: Important in a job: good chances for promotion | V104: Important in a job: a respected job | V105: Important in a job: good hours | V106: Important in a job: an opportunity to use initiative | V107: Important in a job: a useful job for society | V108: Important in a job: generous holidays | V109: Important in a job: meeting people | V110: Important in a job: that you can achieve something | V111: Important in a job: a responsible job | V112: Important in a job: a job that is interesting | V113: Important in a job: a job that meets one´s abilities | V114: Important in a job: none of these | V115: Degree of pride in your work | V116: Job satisfaction | V117: Freedom decision taking in job | V118: Why people work: work is like a business transaction | V119: Why people work: I do the best I can regardless of pay | V120: Why people work: I wouldn’t work if I didn’t have to | V121: Why people work: I wouldn´t work if work interfered my life | V122: Why people work: work most important in my life | V123: Why people work: I never had a paid job | V124: Why people work: don’t know | V125: Fairness: One secretary is paid more | V126: How business and industry should be managed | V127: Following instructions at work | V128: Jobs scarce: Men should have more right to a job than women | V129: Jobs scarce: older people should be forced to retire | V130: Jobs scarce: Employers should give priority to (nation) people than immigrants | V131: Unfair to give work to handicapped people when able bodied people can´t find jobs | V132: Satisfaction with financial situation of household | V133: Thinking about meaning and purpose of life | V134: Thinking about death | V135: Life is meaningful because God exits | V136: Try to get the best out of life | V137: Death is inevitable | V138: Death has meaning if you believe in God | V139: Death is a natural resting point | V140: Sorrow has meaning if you believe in God | V141: Life has no meaning | V142: Statement: good and evil | V143: Belong to religious denomination | V144: Religious denomination | V145: Which former religious denomination | V146: Raised religiously | V147: How often do you attend religious services | V148: Important: Religious service birth | V149: Important: Religious service marriage | V150: Important: Religious service death | V151: Religious person | V152: Churches give answers: moral problems | V153: Churches give answers: the problems of family life | V154: Churches give answers: people´s spiritual needs | V155: Churches give answers: the social problems | V156: Churches speak out on: disarmament | V157: Churches speak out on: abortion | V158: Churches speak out on: third world problems | V159: Churches speak out on: extramarital affairs | V160: Churches speak out on: unemployment | V161: Churches speak out on: racial discrimination | V162: Churches speak out on: euthanasia | V163: Churches speak out on: homosexuality | V164: Churches speak out on: ecology and environmental issues | V165: Churches speak out on: government policy | V166: Believe in: God | V167: Believe in: life after death | V168: Believe in: people have a soul | V169: Believe in: devil | V170: Believe in: hell | V171: Believe in: heaven | V172: Believe in: sin | V173: Believe in: resurrection of the dead | V174: Believe in: re-incarnation | V175: Personal God vs. Spirit or Life Force | V176: How important is God in your life | V177: Get comfort and strength from religion | V178: Moments of prayer, meditation... | V179: Pray to God outside of religious services (ii) | V180: Satisfaction with home life | V181: Marital status | V182: Have you been married before | V183: Sharing with partner: attitudes towards religion | V184: Sharing with partner: moral standards | V185: Sharing with partner: social attitudes | V186: Sharing with partner: political views | V187: Sharing with partner: sexual attitudes | V188: Sharing with partner: no sharing attitudes | V189: Sharing with partner: don´t know or missing | V190: Sharing with parents: attitudes towards religion | V191: Sharing with parents: moral standards´ | V192: Sharing with parents: social attitudes | V193: Sharing with parents: political views | V194: Sharing with parents: sexual attitudes | V195: Sharing with parents: no sharing attitudes | V196: Sharing with parents: don´t know or missing | V197: Enjoy sexual freedom | V198: Important for succesful marriage: Faithfulness | V199: Important for succesful marriage: Adequate income | V200: Important for succesful marriage: Same social background | V201: Important for succesful marriage: Respect and appreciation | V202: Important for succesful marriage: Religious beliefs | V203: Important for succesful marriage: Good housing | V204: Important for succesful marriage: Agreement on politics | V205: Important in succesful marriage: Understanding and tolerance | V206: Important for succesful marriage: Apart from in-laws | V207: Important for succesful marriage: Happy sexual relationship | V208: Important for succesful marriage: Sharing household chores | V209: Important for succesful marriage: Children | V210: Important for succesful marriage: Tastes and interests in common | V211: How many children do you have | V212: How many are still living at home | V213: Ideal number of children | V214: Child needs a home with father and mother | V215: A woman has to have children to be fulfilled | V216: Marriage is an out-dated institution | V217: Woman as a single parent | V218: Relationship working mother | V219: Pre-school child suffers with working mother | V220: Women want a home and children | V221: Being a housewife just as fulfilling | V222: Job best way for women to be independent | V223: Husband and wife should both contribute to income | V224: Respect and love for parents | V225: Parents responsibilities to their children | V226: Important child qualities: good manners | V227: Important child qualities: independence | V228: Important child qualities: hard work | V229: Important child qualities: feeling of responsibility | V230: Important child qualities: imagination | V231: Important child qualities: tolerance and respect for other people | V232: Important child qualities: thrift saving money and things | V233: Important child qualities: determination perseverance | V234: Important child qualities: religious faith | V235: Important child qualities: unselfishness | V236: Important child qualities: obedience | V237: Abortion when the mothers health is at risk | V238: Abortion when child physically handicapped | V239: Abortion when woman not married | V240: Abortion if not wanting more children | V241: Interest in politics | V242: Political action: signing a petition | V243: Political action: joining in boycotts | V244: Political action: attending lawful/peaceful demonstrations | V245: Political action: joining unofficial strikes | V246: Political action: occupying buildings or factories | V247: Freedom or equality | V248: Self positioning in political scale | V249: Basic kinds of attitudes concerning society | V250: Income equality | V251: Private vs state ownership of business | V252: Government responsibility | V253: Job taking of the unemployed | V254: Competition good or harmful | V255: Hard work brings success | V256: Wealth accumulation | V257: Aims of country: first choice | V258: Aims of country: second choice | V259: Aims of respondent: first choice | V260: Aims of respondent: second choice | V261: Most important: first choice | V262: Most important: second choice | V263: Willingness to fight for country | V264: Future changes: Less emphasis on money and material possessions | V265: Future changes: Less importance placed on work | V266: Future changes: More emphasis on technology | V267: Future changes: More emphasis on individual | V268: Future changes: Greater respect for authority | V269: Future changes: More emphasis on family life | V270: Future changes: A simple and more natural lifestyle | V271: Opinion about scientific advances | V272: Confidence: Churches | V273: Confidence: Armed Forces | V274: Confidence: Education System | V275: Confidence: Justice System | V276: Confidence: The Press | V277: Confidence: Labour Unions | V278: Confidence: The Police | V279: Confidence: Parliament | V280: Confidence: The Civil Services | V281: Confidence: Major Companies | V282: Confidence: Social Security System | V283: Confidence: Television | V283B: Confidence: The European Union | V284: Confidence: NATO | V285: Confidence: The Political Parties | V286: What thing are you proud of in your country -1st | V287: What thing are you proud of in your country -2nd | V288: Country is run by big interest vs. for all people’s benefit | V289: Confidence: The Government | V290: Approval: Ecology movement or nature protection | V291: Approval: Anti-nuclear energy movement | V292: Approval: Disarmament movement | V293: Approval: Human rights movement | V294: Approval: Women’s movement | V295: Approval: Anti-apartheid movement | V296: Justifiable: claiming government benefits | V297: Justifiable: avoiding a fare on public transport | V298: Justifiable: cheating on taxes | V299: Justifiable: buy stolen goods | V300: Justifiable: joyriding | V301: Justifiable: taking soft drugs | V302: Justifiable: keeping money that you have found | V303: Justifiable: lying | V304: Justifiable: adultery | V305: Justifiable: sex under the legal age of consent | V306: Justifiable: someone accepting a bribe | V307: Justifiable: homosexuality | V308: Justifiable: prostitution | V309: Justifiable: abortion | V310: Justifiable: divorce | V311: Justifiable: fighting with the police | V312: Justifiable: euthanasia | V313: Justifiable: suicide | V314: Justifiable: failing to report damage you’ve done accidentally to a parked vehicle | V315: Justifiable: threatening workers who refuse to join a strike | V316: Justifiable: killing in self-defence | V317: Justifiable: political assassination | V318: Justifiable: throwing away litter | V319: Justifiable: driving under influence of alcohol | V320: Geographical groups belonging to first | V321: Geographical groups belonging to second | V322: How proud of nationality | V323: Major changes in life | V324: New and old ideas | V325: Personal characteristics: Changes, worry or welcome possibility | V326: Personal characteristics: I usually count on being successful in everything I do | V327: Personal characteristics: I enjoy convincing others of my opinion | V328: Personal characteristics: I serve as a model for others | V329: Personal characteristics: I am good at getting what I want | V330: Personal characteristics: I own many things others envy me for | V331: Personal characteristics: I like to assume responsibility | V332: Personal characteristics: I am rarely unsure about how I should behave | V333: Personal characteristics: I often give others advice | V334: Personal characteristics: None of the above | V335: The economic system needs fundamental changes | V336: Our government should be made much more open to the public | V337: Allow more freedom for individuals | V338: I could do nothing about an unjust law | V339: Political reform is moving too rapidly | V340: How much do you trust your family | V341: Trust: Other people in country | V342: Trust: French | V344: Trust: Americans | V346: Trust: Russians | V347: Trust: Chinese | V347_01: Trust: Italians | V347_02: Trust: Latin Americans | V347_03: Trust: Japanese | V347_04: Trust: Blacks | V347_05: Trust: Germans | V347_06: Trust: Mapuche Indians | V347_07: Trust: Pascuences | V347_08: Trust: Argentines | V347_09: Trust: Peruvians | V347_10: Trust: Chinese Zhuan Nationality | V347_11: Trust: Chinese Hui Nationality | V347_12: Trust: English | V347_13: Trust: Slovaks | V347_14: Trust: Gypsies | V347_15: Trust: Poles | V347_16: Trust: Indian Hindus | V347_17: Trust: Indian Non-Hindus | V347_18: Trust: Pakistanis | V347_19: Trust: Nepalis | V347_20: Trust: Korean residents in Japan | V347_21: Trust: Chinese residents in Japan | V347_22: Trust: Mestizo | V347_23: Trust: Indians | V347_24: Trust: Central Americans | V347_25: Trust: Hausas | V347_26: Trust: Igbos | V347_27: Trust: Yorubas | V347_28: Trust: Ghanaians | V347_29: Trust: Czechs | V347_30: Trust: West Germans | V347_31: Trust: Your friends | V347_32: Trust: Your neighborhood | V347_33: Trust: White South Africans | V347_34: Trust: Black South Africans | V347_35: Trust: Coloured South Africans | V347_36: Trust: Asian South Africans | V347_37: Trust: Moroccans | V347_38: Trust: Portuguese | V347_39: Trust: Koreans | V347_40: Trust: Soviet Union people | V348: Born in this country: birth country | V349: When came to country | V350: Geographical groups belonging to least of all | V351: Which party would you vote for: first choice | V352: Which party would you vote for: second choice | V353: Sex | V354: Year of birth | V355: Age | V356: What age did you complete your education | V357: Do you live with your parents | V358: Employment status | V359: Profession/job | V360: Are you the chief wage earner in your house | V361: Is the chief wage earner employed now | V362: Chief wage earner profession/job | V363: Scale of incomes | V363CS: Income (country specific) | V364: Socio-economic status of respondent | V365: Time at the end of interview | V366: Total length of interview | V367: Respondent interested during the interview | V368: Size of town | V368CS: Size of town (country specific) | V369: Ethnic group | V370: Region where the interview was conducted | V372: Type of habitat | V373: Language in which interview was conducted | V375: Highest educational level attained | V376: Weight | V376A: Weight with split ups | V375CS: Education (country specific) | G001_CS: Geographical groups belonging to first (country specific) | G002_CS: Geographical groups belonging to second (country specific) | V377: Year survey | S024: Nation-Wave | S025: Nation Year | Y001: Materialist/postmaterialist 12-item index | Y002: Post-materialist index (4-item) | Y003: Autonomy Index | SACSECVAL: Overall Secular Values | SECVALWGT: Weight for overal secular values | RESEMAVAL: Emancipative values | RESEMAWEIGHT: Weight for Emancipative values | I_AUTHORITY: Overall Secular Values-1: Inverse respect for authority | I_NATIONALISM: Overall Secular Values-1: Inverse national pride | I_DEVOUT: Overall Secular Values-1: Inverse devoutness | DEFIANCE: Overall Secular Values-1: Defiance sub-index | WEIGHT1A: Overall Secular Values-1: Weight1a | I_RELIGIMP: Overall Secular Values-2: Inverse import of relig | I_RELIGBEL: Overall Secular Values-2: Inverse relig person | I_RELIGPRAC: Overall Secular Values-2: Inverse relig practice | DISBELIEF: Overall Secular Values-2: Disbelief sub-index | WEIGHT2A: Overall Secular Values-2: Weight2a | I_NORM1: Overall Secular Values-3: Inverse norm conform1 | I_NORM2: Overall Secular Values-3: Inverse norm conform2 | I_NORM3: Overall Secular Values-3: Inverse norm conform3 | RELATIVISM: Overall Secular Values-3: Relativism | WEIGHT3A: Overall Secular Values-3: Weight | I_TRUSTARMY: Overall Secular Values-4: Inverse trust in army | I_TRUSTPOLICE: Overall Secular Values-4: Inverse trust in police | I_TRUSTCOURTS: Overall Secular Values-4: Inverse trust in courts | SCEPTICISM: Overall Secular Values-4: Scepticism index | WEIGHT4A: Overall Secular Values-4: Weight 4a | I_INDEP: Emancipative Values-1: Independence as kid quality | I_IMAGIN: Emancipative Values-1: Imagination as kid quality | I_NONOBED: Emancipative Values-1: Obedience not kid quality | AUTONOMY: Emancipative Values-1: Autonomy subindex | WEIGHT1B: Emancipative Values-1: Weight1b | I_WOMJOB: Emancipative Values-2: Gender equality: job | I_WOMPOL: Emancipative Values-2: Gender equality: politics | I_WOMEDU: Emancipative Values-2: Gender equality: education | EQUALITY: Emancipative Values-2: Equality sub-index | WEIGHT2B: Emancipative Values-2: Weight2b | I_HOMOLIB: Emancipative Values-3: Homosexuality acceptance | I_ABORTLIB: Emancipative Values-3: Abortion acceptable | I_DIVORLIB: Emancipative Values-3: Divorce acceptable | CHOICE: Emancipative Values-3: Choice sub-index | WEIGHT3B: Emancipative Values-3: Weight3b | I_VOICE1: Emancipative Values-4: Voice 1 | I_VOICE2: Emancipative Values-4: Voice2 | I_VOI2_00: Emancipative Values-4: Voi2_00 (auxiliary) | VOICE: Emancipative Values-4: Voice sub-index | WEIGHT4B: Emancipative Values-4: Weight 4b | S001: Study | S007: Unified respondent number | S018: Equilibrated weight-1000 | S019: 1500 equilibrated weight | S021: Country - wave - study - set - year | COW: COW Country Code | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 156 | 156 | 1 | 1 | 2 | 2 | 3 | 4 | 4 | 2 | 1 | 2 | 1 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | 2 | 2 | -4 | 2 | -4 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 8 | 9 | 3 | 4 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 10 | 8 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 3 | 3 | 1 | 1 | 3 | 5 | 3 | 2 | 1 | 1 | 1 | -4 | 1 | -4 | 3 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 1 | 2 | 2 | 5 | 10 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 3 | -4 | -4 | -4 | -4 | -4 | 1 | -4 | 1 | 9 | 2 | 2 | 10 | 1 | 6 | 10 | 3 | 1 | 3 | 4 | 2 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 2 | 2 | 2 | -1 | 2 | 2 | -1 | -4 | -1 | -1 | 2 | 4 | 8 | 2 | -4 | 2 | 3 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 5 | 5 | 1 | 1 | 1 | 1 | 1 | 8 | 8 | 8 | 10 | 1 | 1 | 5 | 10 | -4 | 1 | 1 | 2 | 1 | 2 | 9 | 3 | 9 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 3 | -4 | 3 | 2 | -4 | -4 | -4 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 3 | 4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1962 | 28 | 10 | -4 | 1 | 1 | 2 | 1 | 1 | 5 | -4 | 2 | 1710 | 201 | 1 | 8 | -4 | 156002 | 156001 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 3 | 2 | 2 | 0.361750 | 0.915 | 0.717338 | 0.915 | 0.0 | 0.33 | 0.33 | 0.220000 | 1.00 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 0.00 | 0.33 | 0.66 | 0.330000 | 1.00 | 1 | 1 | 1 | 1.000000 | 1 | 1.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.777778 | 0.777778 | 0.518519 | 1.00 | 0.33 | 1.0 | 0.665 | 0.665 | 1.0 | 2 | 1560240001 | 1 | 1.5 | 15602211990 | 710 |
| 1 | 2 | 156 | 156 | 2 | 3 | 2 | 2 | 2 | 3 | 4 | 1 | 2 | 2 | 2 | 3 | 3 | 1 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 3 | 3 | 3 | 4 | 3 | 1 | 3 | 3 | 3 | 4 | 4 | 3 | 1 | 2 | -4 | 1 | -4 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 6 | 3 | 3 | 4 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 6 | 6 | 2 | 1 | 2 | 2 | 1 | 2 | 2 | 1 | 4 | 2 | 1 | 1 | 1 | 3 | 4 | 2 | 2 | 1 | 1 | 1 | -4 | 1 | -4 | 2 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | 2 | 2 | 5 | 3 | 6 | 3 | -3 | -3 | -3 | -3 | -3 | -3 | -3 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 3 | 1 | 0 | -3 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | -4 | 2 | 8 | 2 | 2 | 8 | 2 | 5 | 5 | 1 | 4 | 2 | 4 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | -1 | -1 | 2 | -1 | 2 | -1 | 3 | -2 | -1 | -1 | 3 | -4 | 2 | 2 | -2 | 7 | 7 | -2 | -4 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 3 | 2 | 1 | 3 | 9 | 9 | 7 | 4 | 1 | 1 | 9 | 9 | -1 | 10 | 8 | 3 | 2 | 8 | -4 | 3 | 3 | 3 | 2 | 3 | 8 | 8 | 8 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | -4 | 3 | 3 | -4 | -4 | -4 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1963 | 27 | 10 | -4 | 1 | 2 | 2 | 1 | 3 | 4 | -4 | 2 | 2315 | 215 | 1 | 8 | -4 | 156002 | 156001 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 3 | 3 | 1 | 0.853444 | 0.830 | 0.692741 | 0.915 | 1.0 | 0.66 | 0.33 | 0.663333 | 1.00 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 1.0 | 1.0 | 1.0 | 1.000000 | 1.0 | NaN | 0.66 | NaN | NaN | 0.66 | 1 | 1 | 1 | 1.000000 | 1 | 0.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.888889 | 0.888889 | 0.592593 | 1.00 | 1.00 | 0.0 | 0.500 | 0.500 | 1.0 | 2 | 1560240002 | 1 | 1.5 | 15602211990 | 710 |
| 2 | 2 | 156 | 156 | 3 | 2 | 1 | 3 | 2 | 3 | 4 | 1 | 4 | 3 | 2 | 2 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | 1 | 2 | -4 | 2 | -4 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 3 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 7 | 6 | 3 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 3 | 5 | 5 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 3 | 3 | 2 | 3 | 1 | 1 | -4 | 1 | -4 | 2 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | 2 | 2 | 5 | 6 | 6 | 3 | -3 | -3 | -3 | -3 | -3 | -3 | -3 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 3 | 1 | 3 | 2 | 3 | 1 | 2 | 1 | 2 | 2 | 1 | 0 | -3 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | -4 | 2 | 9 | 5 | 5 | 6 | 2 | 8 | 3 | 1 | 4 | 2 | 4 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 4 | -1 | 2 | 3 | 3 | -1 | 2 | 2 | 3 | 3 | 2 | -4 | -1 | -1 | 2 | 7 | 7 | -1 | -4 | 1 | 2 | 2 | 2 | 3 | 2 | 4 | 3 | 5 | 5 | 2 | 1 | 5 | 8 | 6 | 7 | 4 | 4 | 3 | 8 | -1 | -1 | 8 | 3 | 4 | 4 | 9 | -4 | 6 | 4 | 3 | 2 | 3 | 6 | 5 | 7 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | -4 | 3 | 3 | -4 | -4 | -4 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1964 | 26 | 10 | -4 | 1 | 3 | 2 | 1 | 2 | 4 | -4 | 2 | 2235 | 145 | 1 | 8 | -4 | 156002 | 156001 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 3 | 3 | 2 | 0.767381 | 0.830 | 0.695135 | 0.830 | 1.0 | 0.66 | 0.33 | 0.663333 | 1.00 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 1.0 | 1.0 | 1.0 | 1.000000 | 1.0 | NaN | 0.33 | 0.66 | 0.509190 | 0.66 | 1 | 1 | 1 | 1.000000 | 1 | 0.0 | NaN | NaN | NaN | 0.66 | 0.333333 | 0.777778 | NaN | 0.600444 | 0.66 | 1.00 | 0.0 | 0.500 | 0.500 | 1.0 | 2 | 1560240003 | 1 | 1.5 | 15602211990 | 710 |
| 3 | 2 | 156 | 156 | 4 | 1 | 2 | 2 | 1 | 4 | 3 | 2 | 1 | 3 | 2 | 2 | 2 | 3 | 1 | 3 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 3 | 5 | 3 | 1 | 1 | 2 | 4 | 3 | 5 | 1 | 2 | 2 | -4 | 1 | -4 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 2 | 10 | 10 | 2 | 3 | 1 | 1 | 2 | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 8 | 8 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 3 | 3 | 1 | 2 | 3 | 8 | 1 | 3 | 1 | 1 | 1 | -4 | 2 | -4 | 3 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 1 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | 2 | 1 | 4 | 10 | 6 | 3 | -3 | -3 | -3 | -3 | -3 | -3 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 1 | 3 | 3 | 1 | 3 | 3 | 3 | 1 | 3 | 2 | 2 | 3 | 1 | 0 | -3 | 0 | 1 | 2 | 2 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 3 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 3 | -4 | -4 | -4 | -4 | -4 | 2 | -4 | 1 | 10 | 1 | 1 | 9 | 1 | 8 | 8 | 1 | 2 | 1 | 4 | 1 | -1 | 1 | 3 | 3 | 1 | 1 | 2 | 3 | 2 | 2 | 4 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 2 | 3 | 3 | -4 | 2 | 2 | 3 | 4 | 1 | -1 | -4 | 1 | 1 | 2 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 4 | 1 | 1 | -1 | -1 | 7 | 10 | 7 | 1 | 2 | 10 | -4 | 1 | 1 | 2 | 1 | 4 | 2 | 1 | 8 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 1 | -4 | 3 | 3 | -4 | -4 | -4 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 3 | 4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1969 | 21 | 10 | -4 | 6 | 9 | 2 | 1 | 1 | 6 | -4 | 1 | 1920 | 205 | 1 | 8 | -4 | 156002 | 156001 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | -2 | 2 | 2 | 0.556698 | 0.915 | NaN | 0.830 | 0.5 | 1.00 | 0.33 | 0.610000 | 1.00 | 0.66 | 1.0 | NaN | 0.733460 | 0.66 | 0.0 | 0.0 | 1.0 | 0.333333 | 1.0 | 0.33 | 0.66 | 0.66 | 0.550000 | 1.00 | 1 | 0 | 1 | 0.666667 | 1 | 1.0 | NaN | NaN | NaN | 0.66 | 0.000000 | NaN | NaN | NaN | 0.66 | 0.33 | 0.0 | 0.165 | 0.165 | 1.0 | 2 | 1560240004 | 1 | 1.5 | 15602211990 | 710 |
| 4 | 2 | 156 | 156 | 5 | 2 | 1 | 2 | 3 | 1 | 4 | 2 | 3 | 2 | 2 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 5 | 4 | 1 | 4 | 4 | 4 | 2 | 1 | 5 | 4 | 3 | 4 | 4 | 3 | 2 | 2 | -4 | 1 | -4 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 9 | 10 | 4 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 10 | 9 | 2 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 3 | 1 | 1 | 1 | 3 | 10 | 3 | 4 | 1 | 1 | 1 | -4 | 1 | -4 | 3 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 2 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 1 | 2 | 2 | 5 | 10 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 | 3 | 2 | 1 | 3 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 2 | 3 | 1 | 1 | 2 | 3 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | -4 | -4 | -4 | -4 | -4 | 3 | -4 | 2 | 7 | 8 | 6 | 6 | 4 | 2 | 9 | 2 | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 3 | 1 | 2 | 3 | 1 | 2 | 1 | 4 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 3 | 1 | -4 | 4 | 4 | 1 | 8 | 1 | 2 | -4 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 3 | 1 | 1 | 1 | 8 | -4 | 1 | 1 | 3 | 1 | 1 | 9 | 4 | 2 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 2 | -4 | 3 | 3 | -4 | -4 | -4 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 3 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1947 | 43 | 10 | -4 | 1 | 8 | 1 | -3 | -3 | 2 | -4 | 3 | 1535 | 150 | 1 | 8 | -4 | 156002 | 156001 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 2 | 2 | 1 | 0.335083 | 0.915 | 0.361412 | 0.915 | 1.0 | 0.00 | 0.33 | 0.443333 | 1.00 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 0.00 | 0.00 | 0.00 | 0.000000 | 1.00 | 1 | 0 | 1 | 0.666667 | 1 | 0.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.333333 | 0.000000 | 0.111111 | 1.00 | 0.33 | 0.0 | 0.165 | 0.165 | 1.0 | 2 | 1560240005 | 1 | 1.5 | 15602211990 | 710 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 995 | 2 | 156 | 156 | 996 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 1 | 5 | 5 | 1 | 1 | 5 | 5 | 5 | 2 | 5 | 4 | 1 | 2 | 2 | -4 | 2 | -4 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 9 | 7 | 4 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 10 | 5 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 3 | 1 | 3 | 10 | 3 | 3 | 1 | 1 | 1 | -4 | 3 | -4 | 1 | 3 | 1 | 49 | 0 | 1 | 6 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 10 | 1 | 1 | 4 | 10 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 3 | 1 | 2 | 1 | 3 | 6 | 6 | 4 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 3 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 3 | -4 | -4 | -4 | -4 | -4 | 3 | -4 | 3 | 10 | 5 | 3 | 1 | 1 | 5 | 5 | 4 | 2 | 1 | 3 | 2 | 4 | 1 | -1 | 3 | 1 | 3 | -1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | 3 | 1 | -1 | 3 | 3 | -4 | -1 | -1 | -1 | 6 | 1 | 2 | -4 | 1 | 3 | 2 | 4 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 6 | 1 | 1 | 1 | 10 | -4 | 1 | 1 | 3 | 2 | 1 | 10 | 5 | 3 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 2 | -4 | -1 | -1 | -4 | -4 | -4 | -1 | -4 | -4 | -4 | -4 | -4 | -4 | -1 | 2 | -1 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602003 | -4 | -4 | 1 | 1948 | 42 | 5 | -4 | 1 | 7 | 1 | -3 | -3 | 2 | -4 | 4 | 1039 | 150 | 3 | 6 | -4 | 156004 | 156041 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 1 | 1 | -1 | 0.213500 | 0.915 | 0.248148 | 0.915 | NaN | 0.00 | 0.33 | 0.246220 | 0.66 | 0.33 | 0.0 | 0.833333 | 0.387778 | 1.00 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 0.00 | 0.66 | 0.00 | 0.220000 | 1.00 | 0 | 0 | 1 | 0.333333 | 1 | 0.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.555556 | 0.000000 | 0.185185 | 1.00 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 2 | 1560240996 | 1 | 1.5 | 15602211990 | 710 |
| 996 | 2 | 156 | 156 | 997 | 1 | 1 | 2 | 3 | 2 | 4 | 1 | 2 | 1 | 1 | 3 | 4 | 3 | 4 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 4 | 5 | 3 | 5 | 1 | 1 | 4 | 5 | 3 | 5 | 5 | 3 | 1 | 2 | -4 | 1 | -4 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 10 | 10 | 2 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 9 | 8 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 4 | 3 | 3 | 1 | 1 | 3 | 9 | 2 | 4 | 1 | 2 | 1 | -4 | 1 | -4 | 3 | 3 | 1 | 49 | 0 | 1 | 8 | 2 | 1 | 1 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 1 | 2 | 2 | 5 | 10 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 2 | 3 | 1 | 3 | 3 | 1 | 2 | 3 | 2 | 1 | 3 | 2 | 3 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 3 | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | -4 | -4 | -4 | -4 | -4 | 3 | -4 | 2 | 8 | 9 | 5 | 3 | 1 | 3 | 9 | 1 | 4 | 2 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 2 | 2 | 1 | 2 | 1 | 4 | 2 | 1 | 2 | 2 | 3 | 2 | 1 | 2 | 3 | 1 | -4 | 3 | 4 | 1 | 5 | 8 | 2 | -4 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 | 9 | 1 | 8 | 1 | 1 | 1 | 10 | -4 | 1 | 1 | 3 | 2 | 1 | 7 | 9 | 10 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 3 | -4 | 4 | 4 | -4 | -4 | -4 | 4 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 3 | 4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602003 | -4 | -4 | 2 | 1953 | 37 | 10 | -4 | 1 | 3 | 2 | 1 | 3 | 6 | -4 | 2 | 1631 | 230 | 2 | 5 | -4 | 156004 | 156041 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 2 | 2 | 2 | 0.375917 | 0.915 | 0.555454 | 0.915 | 0.5 | 0.00 | 0.33 | 0.276667 | 1.00 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 0.33 | 0.33 | 0.33 | 0.330000 | 1.00 | 1 | 0 | 1 | 0.666667 | 1 | 1.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.888889 | 0.888889 | 0.592593 | 1.00 | 0.66 | 0.0 | 0.330 | 0.330 | 1.0 | 2 | 1560240997 | 1 | 1.5 | 15602211990 | 710 |
| 997 | 2 | 156 | 156 | 998 | 1 | 1 | 2 | 3 | 1 | 2 | 1 | 2 | 2 | 2 | 4 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 5 | 5 | 5 | 5 | 5 | 2 | 2 | 2 | 5 | 4 | 5 | 5 | 5 | 5 | 2 | 2 | -4 | 2 | -4 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | 3 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 8 | 8 | 3 | 4 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 5 | 6 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 5 | 1 | 3 | 3 | 2 | 1 | -4 | 1 | -4 | 3 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 2 | 6 | 2 | 2 | 4 | 8 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | -4 | -4 | -4 | -4 | -4 | 1 | -4 | 1 | 9 | 1 | 2 | 5 | 1 | 1 | 8 | 1 | 4 | 2 | 4 | 1 | 3 | 1 | 2 | 3 | 1 | 1 | 3 | 1 | 2 | 1 | 2 | 1 | 3 | 4 | 3 | 3 | 4 | 4 | 3 | 3 | 4 | -4 | 3 | 3 | 3 | 8 | 4 | 1 | -4 | 2 | 2 | 2 | 1 | 2 | 1 | 6 | 3 | 3 | 5 | 3 | 1 | 1 | 8 | 9 | 9 | 1 | 1 | 2 | 9 | 10 | 8 | 9 | 6 | 6 | 1 | 10 | -4 | 1 | 1 | 3 | 4 | 3 | 5 | 8 | 9 | 1 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 2 | -4 | 3 | 3 | -4 | -4 | -4 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 3 | 4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1962 | 28 | 10 | -4 | 1 | 1 | 1 | -3 | -3 | 2 | -4 | 4 | 2135 | 140 | 3 | 6 | -4 | 156002 | 156041 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 3 | 3 | 2 | 0.642849 | 0.915 | 0.704037 | 0.915 | 1.0 | 0.66 | 0.33 | 0.663333 | 1.00 | 0.33 | 1.0 | NaN | 0.574730 | 0.66 | 1.0 | 1.0 | 0.0 | 0.666667 | 1.0 | 0.00 | 1.00 | 1.00 | 0.666667 | 1.00 | 1 | 1 | 1 | 1.000000 | 1 | 0.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.888889 | 1.000000 | 0.629630 | 1.00 | 1.00 | 0.0 | 0.500 | 0.500 | 1.0 | 2 | 1560240998 | 1 | 1.5 | 15602211990 | 710 |
| 998 | 2 | 156 | 156 | 999 | 1 | 1 | 2 | 2 | 3 | 4 | 2 | 3 | 2 | 2 | 2 | 3 | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 5 | 5 | 5 | 4 | 5 | 3 | 1 | 5 | 5 | 4 | 4 | 3 | 1 | 1 | 2 | -4 | 1 | -4 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | -4 | -4 | -4 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 1 | 6 | 8 | 3 | 4 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 3 | 7 | 4 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 3 | 2 | 2 | 3 | 8 | 1 | 2 | 1 | 1 | 1 | -4 | 1 | -4 | 3 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 2 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 4 | 1 | 2 | 2 | 5 | 9 | 1 | 2 | 1 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | 1 | 3 | 2 | 1 | 2 | 3 | 2 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 3 | 1 | 2 | 3 | 2 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 3 | -4 | -4 | -4 | -4 | -4 | 2 | -4 | 2 | 8 | 6 | 5 | 4 | 5 | 9 | 7 | 1 | 4 | 3 | 1 | 1 | 3 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 2 | 1 | 4 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 3 | 2 | 2 | -4 | 3 | 3 | 2 | 8 | 2 | 2 | -4 | 2 | 1 | 3 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | 1 | 5 | 3 | 1 | 8 | 1 | 1 | 1 | 10 | -4 | 1 | 1 | 1 | 2 | 2 | 4 | 2 | 8 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | 3 | -4 | 3 | 3 | -4 | -4 | -4 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 3 | 3 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 2 | 1962 | 28 | 8 | -4 | 1 | 4 | 2 | 1 | 5 | 3 | -4 | 3 | 1930 | 130 | 2 | 6 | -4 | 156002 | 156041 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 1 | 1 | 1 | 0.361750 | 0.915 | 0.436778 | 0.915 | 0.0 | 0.33 | 0.33 | 0.220000 | 1.00 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 0.33 | 0.33 | 0.33 | 0.330000 | 1.00 | 1 | 1 | 1 | 1.000000 | 1 | 1.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.444444 | 0.222222 | 0.222222 | 1.00 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 2 | 1560240999 | 1 | 1.5 | 15602211990 | 710 |
| 999 | 2 | 156 | 156 | 1000 | 1 | 1 | 1 | 3 | 4 | 4 | 2 | 2 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 5 | 4 | 2 | 4 | 4 | 4 | 1 | 1 | 4 | 3 | 4 | 4 | 3 | 1 | 2 | 2 | -4 | 2 | -4 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 8 | 8 | 4 | 3 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 8 | 7 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 1 | 2 | 2 | 3 | 7 | 2 | 3 | 1 | 1 | 1 | -4 | 1 | -4 | 3 | 3 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 2 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -1 | 1 | 2 | 2 | 5 | 7 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 1 | 3 | 2 | 3 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 3 | 2 | 3 | 2 | 1 | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 1 | -4 | 2 | 9 | 3 | 3 | 6 | 2 | 3 | 3 | 1 | 4 | 2 | 1 | 1 | 2 | 1 | 3 | 3 | 1 | 1 | -1 | 1 | 1 | 1 | 4 | 2 | 1 | 2 | 2 | 3 | 3 | 3 | 3 | 2 | 1 | -4 | 2 | 2 | 2 | 4 | 5 | 2 | -4 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 7 | 6 | 6 | 10 | 1 | 6 | 6 | 10 | -4 | 1 | 1 | 1 | 2 | 3 | 6 | 3 | 7 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | -4 | -4 | -4 | -4 | -4 | 2 | 2 | -4 | 2 | 3 | -4 | -4 | -4 | 2 | -4 | -4 | -4 | -4 | -4 | -4 | 3 | 3 | 4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | -4 | 1 | -3 | 15602001 | -4 | -4 | 1 | 1946 | 44 | 10 | -4 | 1 | 3 | 1 | -3 | -3 | 6 | -4 | 2 | 1900 | 128 | 2 | 8 | -4 | 156002 | 156041 | -4 | -4 | -4 | 1 | 1 | -4 | -4 | -4 | 1990 | 1562 | 1561990 | 2 | 2 | 2 | 0.459330 | 0.830 | 0.498973 | 0.915 | NaN | 0.66 | 0.33 | 0.500320 | 0.66 | 1.00 | 1.0 | NaN | 0.897000 | 0.66 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.0 | 0.33 | 0.66 | 0.33 | 0.440000 | 1.00 | 1 | 0 | 1 | 0.666667 | 1 | 0.0 | NaN | NaN | NaN | 0.66 | 0.000000 | 0.666667 | 0.555556 | 0.407408 | 1.00 | 0.66 | 0.0 | 0.330 | 0.330 | 1.0 | 2 | 1560241000 | 1 | 1.5 | 15602211990 | 710 |
1000 rows × 477 columns
While I wish there was an easier way to do this, I had to isolate each column manually, not with the handy filter function...
fam_df1990 = df1990[['V4: Important in life: Work',
'V5: Important in life: Family',
'V6: Important in life: Friends',
'V132: Satisfaction with financial situation of household',
'V225: Parents responsibilities to their children',
'V224: Respect and love for parents',
'V236: Important child qualities: obedience']]
fam_df1990 = fam_df1990.rename(columns={"V4: Important in life: Work": "Important in life: Work",
"V5: Important in life: Family": "Important in life: Family",
"V132: Satisfaction with financial situation of household": "Satisfaction with financial situation of household",
"V236: Important child qualities: obedience":"Important child qualities: obedience",
"V6: Important in life: Friends":"Important in life: Friends"})
fam_df1990
| Important in life: Work | Important in life: Family | Important in life: Friends | Satisfaction with financial situation of household | V225: Parents responsibilities to their children | V224: Respect and love for parents | Important child qualities: obedience | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 2 | 2 | 5 | 2 | 1 | 2 |
| 1 | 3 | 2 | 2 | 4 | 2 | 2 | 2 |
| 2 | 2 | 1 | 3 | 3 | 2 | 1 | 2 |
| 3 | 1 | 2 | 2 | 8 | 3 | 2 | 2 |
| 4 | 2 | 1 | 2 | 10 | 1 | 1 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 995 | 2 | 2 | 2 | 10 | 1 | 1 | 2 |
| 996 | 1 | 1 | 2 | 9 | 1 | 1 | 2 |
| 997 | 1 | 1 | 2 | 5 | 2 | 2 | 2 |
| 998 | 1 | 1 | 2 | 8 | 1 | 1 | 2 |
| 999 | 1 | 1 | 1 | 7 | 2 | 1 | 2 |
1000 rows × 7 columns
fam_df1990['Year'] = 1990
columns = ['Important in life: Work', 'Important in life: Family', 'Important child qualities: obedience', 'Important in life: Friends']
fam1990 = fam_df1990.groupby('Year')[columns].mean()
fam1990.reset_index()
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | |
|---|---|---|---|---|---|
| 0 | 1990 | 1.463 | 1.423 | 1.915 | 2.041 |
fam_df1995 = df1995[['V4: Important in life: Family',
'V5: Important in life: Friends',
'V8: Important in life: Work',
'V12: Respect and love for parents',
'V13: Parents responsibilities to their children',
'V64: Satisfaction with financial situation of household',
'V70: One of main goals in life has been to make my parents proud',
'V24: Important child qualities: obedience']]
fam_df1995 = fam_df1995.rename(columns={"V8: Important in life: Work": "Important in life: Work",
"V4: Important in life: Family": "Important in life: Family",
"V64: Satisfaction with financial situation of household": "Satisfaction with financial situation of household",
"V24: Important child qualities: obedience":"Important child qualities: obedience",
"V5: Important in life: Friends":"Important in life: Friends",
"V70: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df1995 = fam_df1995.reset_index()
fam_df1995['Year'] = 1995
columns2 = ['Important in life: Work', 'Important in life: Family', 'Important child qualities: obedience', 'Important in life: Friends', 'Make parents proud']
fam1995 = fam_df1995.groupby('Year')[columns2].mean()
fam1995.reset_index()
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | Make parents proud | |
|---|---|---|---|---|---|---|
| 0 | 1995 | 1.372667 | 1.240667 | 1.707333 | 1.797333 | 1.910667 |
fam_df2001 = df2001[['V4: Important in life: Family',
'V5: Important in life: Friends',
'V8: Important in life: Work',
'V13: Respect and love for parents',
'V14: Parents responsibilities to their children',
'V80: Satisfaction with financial situation of household',
'V24: Important child qualities: obedience',
'V113: One of main goals in life has been to make my parents proud']]
fam_df2001 = fam_df2001.rename(columns={"V8: Important in life: Work": "Important in life: Work",
"V4: Important in life: Family": "Important in life: Family",
"V80: Satisfaction with financial situation of household": "Satisfaction with financial situation of household",
"V24: Important child qualities: obedience":"Important child qualities: obedience",
"V5: Important in life: Friends":"Important in life: Friends",
"V113: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2001 = fam_df2001.reset_index()
fam_df2001['Year'] = 2001
fam2001 = fam_df2001.groupby('Year')[columns2].mean()
fam2001.reset_index()
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | Make parents proud | |
|---|---|---|---|---|---|---|
| 0 | 2001 | 1.536 | 1.403 | 1.852 | 1.892 | 1.835 |
fam_df2007 = df2007[['V4: Important in life: Family',
'V5: Important in life: Friends',
'V8: Important in life: Work',
'V21: Important child qualities: obedience',
'V68: Satisfaction with financial situation of household',
'V64: One of main goals in life has been to make my parents proud']]
fam_df2007 = fam_df2007.rename(columns={"V8: Important in life: Work": "Important in life: Work",
"V4: Important in life: Family": "Important in life: Family",
"V68: Satisfaction with financial situation of household": "Satisfaction with financial situation of household",
"V21: Important child qualities: obedience":"Important child qualities: obedience",
"V5: Important in life: Friends":"Important in life: Friends",
"V64: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2007 = fam_df2007.reset_index()
fam_df2007['Year'] = 2007
fam2007 = fam_df2007.groupby('Year')[columns2].mean()
fam2007.reset_index()
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | Make parents proud | |
|---|---|---|---|---|---|---|
| 0 | 2007 | 1.500251 | 1.201406 | 1.852838 | 1.744852 | 1.489201 |
fam_df2012 = df2012[['V4: Important in life: Family',
'V5: Important in life: Friends',
'V8: Important in life: Work',
'V21: Important child qualities: obedience',
'V59: Satisfaction with financial situation of household',
'V49: One of main goals in life has been to make my parents proud']]
fam_df2012 = fam_df2012.rename(columns={"V8: Important in life: Work": "Important in life: Work",
"V4: Important in life: Family": "Important in life: Family",
"V59: Satisfaction with financial situation of household": "Satisfaction with financial situation of household",
"V21: Important child qualities: obedience":"Important child qualities: obedience",
"V5: Important in life: Friends":"Important in life: Friends",
"V49: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2012 = fam_df2012.reset_index()
fam_df2012['Year'] = 2012
fam2012 = fam_df2012.groupby('Year')[columns2].mean()
fam2012.reset_index()
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | Make parents proud | |
|---|---|---|---|---|---|---|
| 0 | 2012 | 1.656522 | 1.105652 | 1.927391 | 1.530435 | 1.800435 |
fam_df2018 = df2018[['Q1: Important in life: Family',
'Q2: Important in life: Friends',
'Q5: Important in life: Work',
'Q17: Important child qualities: obedience',
'Q50: Satisfaction with financial situation of household',
'Q27: One of main goals in life has been to make my parents proud']]
fam_df2018 = fam_df2018.rename(columns={"Q5: Important in life: Work": "Important in life: Work",
"Q1: Important in life: Family": "Important in life: Family",
"Q50: Satisfaction with financial situation of household": "Satisfaction with financial situation of household",
"Q17: Important child qualities: obedience":"Important child qualities: obedience",
"Q2: Important in life: Friends":"Important in life: Friends",
"Q27: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2018 = fam_df2018.reset_index()
fam_df2018['Year'] = 2018
fam2018 = fam_df2018.groupby('Year')[columns2].mean()
fam2018.reset_index()
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | Make parents proud | |
|---|---|---|---|---|---|---|
| 0 | 2018 | 1.699934 | 1.137022 | 1.925889 | 1.721014 | 2.026021 |
dfall = pd.concat([fam1990, fam1995, fam2001, fam2007, fam2012, fam2018])
dfall = dfall.reset_index()
dfall
| Year | Important in life: Work | Important in life: Family | Important child qualities: obedience | Important in life: Friends | Make parents proud | |
|---|---|---|---|---|---|---|
| 0 | 1990 | 1.463000 | 1.423000 | 1.915000 | 2.041000 | NaN |
| 1 | 1995 | 1.372667 | 1.240667 | 1.707333 | 1.797333 | 1.910667 |
| 2 | 2001 | 1.536000 | 1.403000 | 1.852000 | 1.892000 | 1.835000 |
| 3 | 2007 | 1.500251 | 1.201406 | 1.852838 | 1.744852 | 1.489201 |
| 4 | 2012 | 1.656522 | 1.105652 | 1.927391 | 1.530435 | 1.800435 |
| 5 | 2018 | 1.699934 | 1.137022 | 1.925889 | 1.721014 | 2.026021 |
dfall_long = pd.melt(dfall, id_vars='Year')
dfall_long.set_index(['variable', 'Year'])
#dfall_long = dfall_long[dfall_long['variable_1'] == 'mean']
#dfall_long = dfall_long.dropna()
#dfall_long = dfall_long.sort_values(by='Year').sort_index()
#dfall_long = dfall_long.groupby('variable_0')
| value | ||
|---|---|---|
| variable | Year | |
| Important in life: Work | 1990 | 1.463000 |
| 1995 | 1.372667 | |
| 2001 | 1.536000 | |
| 2007 | 1.500251 | |
| 2012 | 1.656522 | |
| 2018 | 1.699934 | |
| Important in life: Family | 1990 | 1.423000 |
| 1995 | 1.240667 | |
| 2001 | 1.403000 | |
| 2007 | 1.201406 | |
| 2012 | 1.105652 | |
| 2018 | 1.137022 | |
| Important child qualities: obedience | 1990 | 1.915000 |
| 1995 | 1.707333 | |
| 2001 | 1.852000 | |
| 2007 | 1.852838 | |
| 2012 | 1.927391 | |
| 2018 | 1.925889 | |
| Important in life: Friends | 1990 | 2.041000 |
| 1995 | 1.797333 | |
| 2001 | 1.892000 | |
| 2007 | 1.744852 | |
| 2012 | 1.530435 | |
| 2018 | 1.721014 | |
| Make parents proud | 1990 | NaN |
| 1995 | 1.910667 | |
| 2001 | 1.835000 | |
| 2007 | 1.489201 | |
| 2012 | 1.800435 | |
| 2018 | 2.026021 |
gdp = pd.read_csv('/Users/kelsey.schoeman/Box Sync/Forge - Node/World Values Survey/GDP All.csv')
gdp = gdp[gdp['Country Code'] == 'CHN']
gdp = gdp.drop(columns=['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code'])
gdp = gdp.melt()
gdp = gdp.rename(columns={"variable":'Year'})
gdp = gdp[gdp['Year'] >= '1995'].dropna()
gdp['Year'] = pd.to_datetime(gdp['Year'])
gdp = px.line(gdp, x='Year', y='value',
title='The Economic Growth of the Peoples Republic of China',
labels={'value':'GDP ($)'})
gdp
#gdp.update_layout(xaxis = dict(tickmode = 'linear', dtick = 10)
dfall_long = px.line(dfall_long, x='Year', y='value', color='variable', range_y=[0, 3],
title='Family Values in China (1990-2018)',
labels={'value':'Value', 'variable':'Survey Question'},
hover_name='variable')
dfall_long.update_traces(mode='markers+lines')
#Very Important = 3, Rather Important = 2, Not Very Important = 1